JDD

I ended the last post with a question: the next challenge is not building the Judgment Log. It is whether anyone writes in it once the deadline is two hours away. That question only has a useful answer if the artifact is light enough to actually use. So instead of arguing for it further, I want to show it. Take a fictional but familiar scenario. A checkout flow. A promotional window. A promo code validation feature built using AI-assisted development. It shipped. Three weeks later, it broke when the campaign introduced expired codes. In the post-mortem, nobody could answer the three questions that mattered: what did the PM cut and why, what did the designer choose between, and what did the engineer override. ...

In April, Meta employees burned through 73.7 trillion tokens in roughly thirty days. The company found out not because spending crossed some alarming threshold, but because an internal leaderboard, nicknamed Claudeonomics, had turned token consumption into a competition. Employees and teams were ranked by how much they used. The system did exactly what it was built to do: usage went up. What it could never show anyone was whether any of that usage produced something worth the cost. Meta is now dismantling the leaderboard in favor of a centralized monitoring platform called AI Gateway, built to track spending in real time and flag unusual spikes. ...

In 2012, I was given a project at Dalet that I did not fully understand at the time: move the entire company from waterfall to Agile. We were a mid-size product company with engineering teams spread across time zones, and waterfall was doing what waterfall always does at that scale, creating the illusion of control while real problems accumulated in the gaps between phases. The transition worked. And the productivity gains were not incremental. Engineers who had spent months waiting for approval to write code were suddenly shipping every two weeks. The business could see working software, give real feedback, and change direction without blowing up a six-month plan. It felt like the industry had discovered something fundamental. ...

Something broke in the last two years. The pace of change in AI alone has reached a frequency that no person can track without a system: new models, new frameworks, new paradigms, new competitive moves. Add to that the normal demands of a senior role: strategy documents, stakeholder alignment, product decisions, customer conversations. Add personal projects. Add family. Add the nagging sense that something important happened while you were handling something else. ...

In a post I wrote last December, I discussed the production triangle: the constraint that governs every production system, including software. You can optimize for two of three dimensions, time, quantity, and quality, but never all three. Push velocity while adding features, and quality absorbs the cost. Every time, without exception. This constraint is not new. Kahneman mapped the cognitive mechanism in Thinking, Fast and Slow: under time pressure, we disengage System 2, the slow, deliberate reasoning that catches the things that will hurt us later, and default to fast, intuitive pattern-matching. Kuutila et al.’s 2020 systematic review of the software engineering literature confirmed the outcome: increased throughput, decreased quality, and a root cause that almost always traces back to the pressure itself being a product of bad estimation. The triangle is not a theory. It is physics. ...

Gartner published a prediction this week: by 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance gaps that are only identified after production incidents. The root cause they name at Level 3 of their autonomy framework is approval fatigue. Agents execute actions, writing data, sending communications, and modifying configurations, but only after explicit human approval. Under time pressure, that approval becomes reflexive. The control degrades, and the risk compounds in silence. ...

I recently argued on the JFrog blog that trusted AI requires more than model quality. It requires visibility, provenance, governance, and a real system of control around the things models consume, build, and ship. That is the foundation. This post is about what you build on top of it. Because visibility is necessary. Without it, you cannot govern anything. If you cannot see which models are running, where they came from, how they behave, and what they touch, you do not have a governance posture. You have hope dressed up as architecture. ...

A developer recently gave Claude Code write access to a live Meta Ads account. The agent’s read-only analysis was genuinely valuable; it correctly identified the cheapest campaign as having the worst ROI. The insight was good. The judgment about what to do next was absent. The agent executed autonomously, triggered API rate limits through automated publishing, and resulted in the account being permanently banned. The read was right, the write destroyed the business relationship. ...

Anyone Can Prompt. Not Everyone Can Engineer. What the AI coding revolution actually changes, and what it doesn’t. The Translation Layer Is Gone I recently watched an IBM video that made a point I’ve been thinking about ever since. The speaker walked through the entire history of programming languages, from machine code and assembler, through COBOL and FORTRAN, to object-oriented, web, and scripting languages, and made a simple observation: every generation moved a little closer to the way humans actually think and speak. ...

A recent Sequoia piece made an argument that immediately felt familiar to me. Not because it repeated the phrase “Judgment Driven Development” word for word. But because it pointed at the same shift from a different direction. The article is here: Services: The New Software. Its claim is simple: the next generation of great companies may not look like software vendors at all. They may look like software and AI powered service providers. Instead of selling tools to people who do the work, they will sell the work itself. Instead of selling accounting software, they will close the books. Instead of selling legal tooling, they will draft the NDA. Instead of helping a team perform a process faster, they will increasingly perform the process directly. ...